Note: This is a work in progress.
# A Brief Introduction to This File This R file walks through G. Grolemund & H. Wickhams’s online text, “R for Data Science.” Much of the code is sourced directly from the book and credit belongs to the authors. Here, some sections of code are heavily commented so that the beginning R programmer can read through and understand what each line of code does and compare it to their own as they work through the text. Throughout, the book provides the primary and most thorough explanation. For the greatest learning benefit, I suggest you attempt each exercise on your own before looking at the code or write-ups provided here. Of course, there is more than one way to write code and you may find a more elegant solution that you prefer.

For those new to R and RStudio, it may be of additional benefit to knit the document and examine how the code in the Rmd file is visually expressed in the resultant knitted document. For example, see how the ["R for Data Science."](http://r4ds.had.co.nz/index.html) is expressed as a hyperlink in the preceeding paragraph where it was not surrounded by tick-marks and compare that to how the same text is expressed in this paragraph when surrounded by ticks. See also the difference in appearance when knitting to different document types (HTML, PDF, Word).

Tip: If you are using RStudio, click the text next to the orange # box at the bottom of the editor window to easily navigate the code chunks.

Tip: Use the ? before any command to view the documentation on that function. Do this often. For example, type ?setwd to see a description, usage, arguments, and more for the function setwd().

Tip: Find RStudio Cheatsheets at https://www.rstudio.com/resources/cheatsheets/

Chapter 3, Data Visualisation

To really understand ggplot2, I highly recommend reading “The Layered Grammar of Graphics” as suggested at the beginning of Chapter 3.

The `mpg` data frame

str(mpg) # Look at the structure of the mpg data frame

## Classes 'tbl_df', 'tbl' and 'data.frame':    234 obs. of  11 variables:
##  $ manufacturer: chr  "audi" "audi" "audi" "audi" ...
##  $ model       : chr  "a4" "a4" "a4" "a4" ...
##  $ displ       : num  1.8 1.8 2 2 2.8 2.8 3.1 1.8 1.8 2 ...
##  $ year        : int  1999 1999 2008 2008 1999 1999 2008 1999 1999 2008 ...
##  $ cyl         : int  4 4 4 4 6 6 6 4 4 4 ...
##  $ trans       : chr  "auto(l5)" "manual(m5)" "manual(m6)" "auto(av)" ...
##  $ drv         : chr  "f" "f" "f" "f" ...
##  $ cty         : int  18 21 20 21 16 18 18 18 16 20 ...
##  $ hwy         : int  29 29 31 30 26 26 27 26 25 28 ...
##  $ fl          : chr  "p" "p" "p" "p" ...
##  $ class       : chr  "compact" "compact" "compact" "compact" ...

mpg # Look at the first 10 rows of the mpg data frame

## # A tibble: 234 x 11
##    manufacturer      model displ  year   cyl      trans   drv   cty   hwy
##           <chr>      <chr> <dbl> <int> <int>      <chr> <chr> <int> <int>
##  1         audi         a4   1.8  1999     4   auto(l5)     f    18    29
##  2         audi         a4   1.8  1999     4 manual(m5)     f    21    29
##  3         audi         a4   2.0  2008     4 manual(m6)     f    20    31
##  4         audi         a4   2.0  2008     4   auto(av)     f    21    30
##  5         audi         a4   2.8  1999     6   auto(l5)     f    16    26
##  6         audi         a4   2.8  1999     6 manual(m5)     f    18    26
##  7         audi         a4   3.1  2008     6   auto(av)     f    18    27
##  8         audi a4 quattro   1.8  1999     4 manual(m5)     4    18    26
##  9         audi a4 quattro   1.8  1999     4   auto(l5)     4    16    25
## 10         audi a4 quattro   2.0  2008     4 manual(m6)     4    20    28
## # ... with 224 more rows, and 2 more variables: fl <chr>, class <chr>

Hypothesis: There is a negative linear relationship between engine size and fuel efficiency, such that as engine size increases fuel efficiency decreases.

ggplot(data=mpg) + # specify data frame 
    geom_point(mapping = aes(x = displ, y = hwy)) # specify that plot is a scatterplot with displ on the x axis and hwy on the y axis

The plot confirms the hypothesis that there is a negative relationship between engine size and fuel efficiency.

Making graphs with ggplot2

Template:

ggplot(data = <DATA>) + 
  <GEOM_FUNCTION>(mapping = aes(<MAPPINGS>))

Exercises 3.2.4

1. Run ggplot(data = mpg). What do you see?

There are no visible results from the code below.

ggplot(data = mpg)

2. How many rows are in mpg? How many columns?

Based on the output from str(mpg), we see that there are 234 rows and 11 columns in the mpg data frame.

# Alternative means of finding number of rows and columns
nrow(mpg) # Pring the number of rows

## [1] 234

ncol(mpg)

## [1] 11

There are 234 rows and 11 columns in the mpg data frame.

3. What does the drv variable describe? Read the help for ?mpg to find out.

The drv variable describes whether the vehicle is front, rear, or 4-wheel drive.

?mpg

4. Make a scatterplot of hwy vs cyl.

ggplot(data=mpg) +
    geom_point(mapping = aes(x=hwy, y=cyl))

5. What happens if you make a scatterplot of class vs drv? Why is the plot not useful?

The plot is not useful because the variables are categorical and multiple points are plotted atop one another. We are unable to determine from this plot how many observations there are of each class-drive combination.

ggplot(data = mpg) +
    geom_point(mapping = aes(x=class, y=drv))

Aesthetic mappings

Test the hypothesis that the cars highlighted in red are hybrids by mapping car class to an aesthetic.

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = class)) # map class to the color aesthetic so that three variables are now distinguishable in the plot: engine displacement on the x axis, highway miles per gallon on the y axis, and car class by color.

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, size = class))

# Left
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, alpha = class))

# Right
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, shape = class))

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy), color = "blue") # Set the aesthetic outside of aes() to manually assign it to all points

Aesthetic shapes:

Exercises 3.3.1

1. What’s gone wrong with this code? Why are the points not blue?

The points are not blue because the color aesthetic is set inside aes().

# Problematic code
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = "blue"))

# Corrected code
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy), color = "blue")

2. Which variables in mpg are categorical? Which variables are continuous? (Hint: type ?mpg to read the documentation for the dataset). How can you see this information when you run mpg?

To determine what the categorical and continuous variables are, one can either view the tibble by typing mpg or by viewing the documentation ?mpg. One may decide whether a variable is categorical or continuous by checking whether it is stored as a character, integer, or double (floating point integer) value. However, this can lead to miscategorization in some cases. For example, while year is an integer, it is typically considered a whole number, a discrete variable without a meaningful 0 value anchor, and therefore not continuous.

The categorical variables are:

model
year (discrete, rather than categorical)
trans
drv
fl
class

The continuous variables are:

displ
cyl
cty
hwy
year (in this data set, year is treated as an integer variable. Better to consider this “quantitative”, rather than “continuous”)

3.Map a continuous variable to color, size, and shape. How do these aesthetics behave differently for categorical vs. continuous variables?

A continuous variable cannot be mapped to shape. When mapped to size or color, the continuous variable is binned by equal intervals (in this case, intervals of 5 mpg). When mapped to the size aesthetic, points scale by the intervals. Continuous variables when mapped to a color aesthetic are mapped along a gradient scale.

# Mapping a continuous variable to the shape aesthetic
ggplot(data=mpg) +
    geom_point(mapping = aes(x = displ, y = hwy, shape = cty))

# Mapping continuous variables to the color and size aesthetics
ggplot(data=mpg) +
    geom_point(mapping = aes(x = displ, y = hwy, color = cyl, size = cty))

# Mapping categorical variables to size, color, and shape
ggplot(data=mpg) +
    geom_point(mapping = aes(x = displ, y = hwy, size = model, color = class, shape = drv))

4. What happens if you map the same variable to multiple aesthetics?

When the same variable is mapped to multiple aesthetics, it is represented by those aesthetics.

# Mapping the same variable to multiple aesthetics
ggplot(data=mpg) +
    geom_point(mapping = aes(x = displ, y = hwy, color = cty, size = cty)) # Here, city is mapped to the size and color aesthetics

5. What does the stroke aesthetic do? What shapes does it work with? (Hint: use ?geom_point)

According to the R documentation:
“For shapes that have a border (like 21), you can colour the inside and outside separately. Use the stroke aesthetic to modify the width of the border.”

Tip: You can find documentation of available colors here.

?geom_point

# Example using `stroke`
ggplot(data=mpg)+
    geom_point(mapping = aes(x=displ, y=hwy), shape = 21, colour = "darkgreen", fill = "gold", size = 5, stroke = 5) # `size` sets the area of the inside (gold) and `stroke` sets the area of the outline (green)

# Just for fun, let's write short-hand code make the same plot
ggplot(mpg, aes(displ, hwy)) +
  geom_point(shape = 21, colour = "darkgreen", fill = "gold", size = 5, stroke = 5)

6. What happens if you map an aesthetic to something other than a variable name, like aes(colour = displ < 5)?

Setting the color aesthetic to displ < 5 will assign one color to all x-axis (hwy) values < 5 and a different color to x-axis values \(\ge\) 5. Since the color palette is not specified, default colors are used.

ggplot(data=mpg) +
    geom_point(mapping = aes(x = displ, y = hwy, color = displ < 5))

Facets

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) + 
  facet_wrap(~ class, nrow = 2) # This will create a separate plot for each class of vehicle and will fit the plots into 2 rows

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) + 
  facet_grid(drv ~ cyl) # This will create a grid of plots with one plot for each combination of drv and cyl

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) + 
    facet_grid(. ~ cyl) # Use the . to create plots for each level of cylinder (cyl) in the columns dimension. To facet in the rows dimension, use `facet_grid(cyl ~ .)`

Exercises 3.5.1

1. What happens if you facet on a continuous variable?

If faceting is done with a continuous variable, a plot is created for each value for which there is at least one observation.

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) + 
  facet_wrap(~ hwy, nrow = 2)

2. What do the empty cells in plot with facet_grid(drv ~ cyl) mean? How do they relate to this plot?

The empty cells in the plot with facet_grid(drv ~ cyl) indicate that there are no cars with at the intersection of that number of cylinders and that type of drivetrain (e.g. no cars with 5 cylinders and 4-wheel drive). The absence of vehicles corresponding to specific cylinde r-drive combinations is also evident in the second plot. Those intersections in the second plot without a point correspond to the empty cells in the first plot (see again cars with 5 cylinders on the y-axis and 4-wheel drive on the x-axis).

# First plot, with drivetrain and cylinder faceted
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) + 
  facet_grid(drv ~ cyl)

# Second plot, with drivetrain and cylinder represented on the axes of a single plot
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = drv, y = cyl))

3. What plots does the following code make? What does . do?

The first plot shows highway miles per gallon and engine displacement faceted by drivetrain type. The . in the second position specifies that drivetrain type should be displayed in rows. The second plot shows highway miles per gallon and engine displacement faceted by number of cylinders. The . in the first position specifies that number of cylinders should be displayed in columns.

# Plot of highway mpg and engine displacement faceted by drivetrain type
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) +
  facet_grid(drv ~ .)

# The above is the same as the following except that the drivetrain labels shift from right to top aligned. Uncomment and run the code to see the difference.
#ggplot(data = mpg) + 
#  geom_point(mapping = aes(x = displ, y = hwy)) +
#  facet_wrap(~ drv, nrow = 3) 

# Plot of highway mpg and engine displacement faceted by number of cylinders
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) +
  facet_grid(. ~ cyl)

# The above is the same as the following. Uncomment and run the code to see. 
#ggplot(data = mpg) + 
#    geom_point(mapping = aes(x = displ, y = hwy)) +
#    facet_wrap(~ cyl, nrow = 1)

4. Take the first faceted plot in this section. What are the advantages to using faceting instead of the colour aesthetic? What are the disadvantages? How might the balance change if you had a larger dataset?

The advantage of using faceting rather than the color aesthetic is that with separate plots it is easier to see the shape and spread of the data points for each level of the variable. A disadvantage is that it’s difficult to see the overall shape and spread of the observations across levels of the faceted variable. While using the color aesthetic works well with the mpg dataset, with a larger dataset, the likelihood of overlapping data points increases and with enough overlapping observations jittering may be insufficient. It may therefore be preferable to use faceting with large datasets.

# Plot with facets
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) + 
  facet_wrap(~ class, nrow = 2)

# Plot with color aesthetic
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = class))

5. Read ?facet_wrap. What does nrow do? What does ncol do? What other options control the layout of the individual panels? Why doesn’t facet_grid() have nrow and ncol argument?

nrow - specifies the number of rows into which the faceted plots are fitted.
ncol - specifies the number of columns into which the faceted plots are fitted.
facet_grid() does not have nrow or ncol arguments because the number of rows and columns is determined by the number of levels of the row and column facetting variables.

?facet_wrap

Geometric objects

# Scatterplot
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy))

# Smooth geom plot
ggplot(data = mpg) + 
  geom_smooth(mapping = aes(x = displ, y = hwy))

# Use a different linetype for eaxh unique value of drv
ggplot(data = mpg) + 
  geom_smooth(mapping = aes(x = displ, y = hwy, linetype = drv))

# Plot a single geom to display the data
ggplot(data = mpg) +
  geom_smooth(mapping = aes(x = displ, y = hwy))

# Set the `group` aesthetic to `drv` to draw separate geoms for each unique value of the variable
ggplot(data = mpg) +
  geom_smooth(mapping = aes(x = displ, y = hwy, group = drv))

# Set the color aesthetic to `drv` to automatically group the data by drivetrain and distinguish them by color
ggplot(data = mpg) +
  geom_smooth(
    mapping = aes(x = displ, y = hwy, color = drv),
    show.legend = FALSE
  )

# Plot a smooth geom over a scatterplot of the data, the verbose way
ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) +
  geom_smooth(mapping = aes(x = displ, y = hwy))

# The same plot with mappings passed to `ggplot()`
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
  geom_point() + 
  geom_smooth()

# Color to the points by car class
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
  geom_point(mapping = aes(color = class)) + 
  geom_smooth()

# Plot all classes of car, but draw a smooth line geom for only cars of the subcompact class
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
  geom_point(mapping = aes(color = class)) + 
  geom_smooth(data = filter(mpg, class == "subcompact"), se = FALSE)

Exercises 3.6.1

1. What geom would you use to draw a line chart? A boxplot? A histogram? An area chart?

What geom would you use to draw a(n)
- line chart: geom_line() - boxplot: geom_boxplot() - histogram: geom_histogram() - area chart: geom_area()

2. Run this code in your head and predict what the output will look like. Then, run the code in R and check your predictions.

The output will be a scatterplot with engine displacement mapped to the x-axis, miles per gallon highway on the y-axis, and the points colored by type of drivetrain. The scatterplot will be overlaid by one solid smooth line for each drivetrain type and will not expand to indicate the confidence interval. The color of the line for a given type of drivetrain will match the color of the points for the same.

ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = drv)) + 
  geom_point() + 
  geom_smooth(se = FALSE)

3. What does show.legend = FALSE do? What happens if you remove it? Why do you think I used it earlier in the chapter?

show.legend = FALSE indicates that a legend should not be included in the plot. If it is removed, the default is to include a legend if any aesthetics are mapped. It may have been used earlier in the chapter to introduce us to the option, to make the final graph in the set of three match the first two, which did not have legends, or for another unknown reason.

4. What does the se argument to geom_smooth() do?

The se argument to geom_smoot() indicates whether to include confidence intervals around smooth. The default is TRUE.

5. Will these two graphs look different? Why/why not?

The two plots will be identical. In the first polot, the selection o mpg as the data frame from which to draw the variables and the mapping of disp to the x-axis and hwy to the y-axis is specified in the global mappings, within ggplot(). These mappings extend to the following layers, in this case geom_point() and geom_smooth() unless those layers explicitly overwrite the global mappings, which they don’t here. In the second plot, the same mappings are specified as in the global settings of the first plot. Therefore, in both plots, the mappings specified for geom_point() and geom_smooth() are identical, though they are explicitly laid out for each layer in plot 2, whereas they are carried over from the global mappings in plot 1.

# Plot using global mappings
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
  geom_point() + 
  geom_smooth()

# Plot specifcing mappings in each geom layer
ggplot() + 
  geom_point(data = mpg, mapping = aes(x = displ, y = hwy)) + 
  geom_smooth(data = mpg, mapping = aes(x = displ, y = hwy))

6. Recreate the R code necessary to generate the following graphs.

ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
    geom_point() +
    geom_smooth(se=FALSE)

ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
    geom_point() +
    geom_smooth(aes(group = drv), se=FALSE)

ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = drv)) +
    geom_point() + 
    geom_smooth(se=FALSE)

ggplot(data = mpg, mapping = aes( x = displ, y = hwy)) +
    geom_point(aes(color = drv)) +
    geom_smooth(se=FALSE)

ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
    geom_point(aes(color=drv)) + 
    geom_smooth(aes(linetype=drv),se=FALSE)

# ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = drv)) + 
#     geom_point(shape = 21, stroke = 5)
# 
# ggplot(mpg, aes(displ, hwy)) +
#   geom_point(shape = 21, colour = "darkgreen", fill = "gold", size = 5, stroke = 5)

Solutions to G. Grolemund & H. Wickhams’s R for Data Science

Krista DeStasio

7/11/2017

Chapter 3, Data Visualisation

The `mpg` data frame

Making graphs with ggplot2

Exercises 3.2.4

1. Run ggplot(data = mpg). What do you see?

2. How many rows are in mpg? How many columns?

3. What does the drv variable describe? Read the help for ?mpg to find out.

4. Make a scatterplot of hwy vs cyl.

5. What happens if you make a scatterplot of class vs drv? Why is the plot not useful?

Aesthetic mappings

Exercises 3.3.1

1. What’s gone wrong with this code? Why are the points not blue?

2. Which variables in mpg are categorical? Which variables are continuous? (Hint: type ?mpg to read the documentation for the dataset). How can you see this information when you run mpg?

3.Map a continuous variable to color, size, and shape. How do these aesthetics behave differently for categorical vs. continuous variables?

4. What happens if you map the same variable to multiple aesthetics?

5. What does the stroke aesthetic do? What shapes does it work with? (Hint: use ?geom_point)

6. What happens if you map an aesthetic to something other than a variable name, like aes(colour = displ < 5)?

Facets

Exercises 3.5.1

1. What happens if you facet on a continuous variable?

2. What do the empty cells in plot with facet_grid(drv ~ cyl) mean? How do they relate to this plot?

3. What plots does the following code make? What does . do?

4. Take the first faceted plot in this section. What are the advantages to using faceting instead of the colour aesthetic? What are the disadvantages? How might the balance change if you had a larger dataset?

5. Read ?facet_wrap. What does nrow do? What does ncol do? What other options control the layout of the individual panels? Why doesn’t facet_grid() have nrow and ncol argument?

6. When using facet_grid() you should usually put the variable with more unique levels in the columns. Why?

Geometric objects

Exercises 3.6.1

1. What geom would you use to draw a line chart? A boxplot? A histogram? An area chart?

2. Run this code in your head and predict what the output will look like. Then, run the code in R and check your predictions.

3. What does show.legend = FALSE do? What happens if you remove it? Why do you think I used it earlier in the chapter?

4. What does the se argument to geom_smooth() do?

5. Will these two graphs look different? Why/why not?

6. Recreate the R code necessary to generate the following graphs.

Solutions to G. Grolemund & H. Wickhams’s R for Data Science

Krista DeStasio

7/11/2017

Chapter 3, Data Visualisation

The mpg data frame

Making graphs with ggplot2

Exercises 3.2.4

1. Run ggplot(data = mpg). What do you see?

2. How many rows are in mpg? How many columns?

3. What does the drv variable describe? Read the help for ?mpg to find out.

4. Make a scatterplot of hwy vs cyl.

5. What happens if you make a scatterplot of class vs drv? Why is the plot not useful?

Aesthetic mappings

Exercises 3.3.1

1. What’s gone wrong with this code? Why are the points not blue?

2. Which variables in mpg are categorical? Which variables are continuous? (Hint: type ?mpg to read the documentation for the dataset). How can you see this information when you run mpg?

3.Map a continuous variable to color, size, and shape. How do these aesthetics behave differently for categorical vs. continuous variables?

4. What happens if you map the same variable to multiple aesthetics?

5. What does the stroke aesthetic do? What shapes does it work with? (Hint: use ?geom_point)

6. What happens if you map an aesthetic to something other than a variable name, like aes(colour = displ < 5)?

Facets

Exercises 3.5.1

1. What happens if you facet on a continuous variable?

2. What do the empty cells in plot with facet_grid(drv ~ cyl) mean? How do they relate to this plot?

3. What plots does the following code make? What does . do?

4. Take the first faceted plot in this section. What are the advantages to using faceting instead of the colour aesthetic? What are the disadvantages? How might the balance change if you had a larger dataset?

5. Read ?facet_wrap. What does nrow do? What does ncol do? What other options control the layout of the individual panels? Why doesn’t facet_grid() have nrow and ncol argument?

6. When using facet_grid() you should usually put the variable with more unique levels in the columns. Why?

Geometric objects

Exercises 3.6.1

1. What geom would you use to draw a line chart? A boxplot? A histogram? An area chart?

2. Run this code in your head and predict what the output will look like. Then, run the code in R and check your predictions.

3. What does show.legend = FALSE do? What happens if you remove it? Why do you think I used it earlier in the chapter?

4. What does the se argument to geom_smooth() do?

5. Will these two graphs look different? Why/why not?

6. Recreate the R code necessary to generate the following graphs.

The `mpg` data frame